Recurrent neural network based adaptive filtering technique for the extraction of foetal electrocardiogram

2000 ◽  
Vol 36 (20) ◽  
pp. 1744 ◽  
Author(s):  
S. Selvan ◽  
R. Srinivasan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Adaptive Filtering ◽  
Filtering Technique

scholarly journals Adaptive filtering based on recurrent neural networks

2003 ◽  
Vol 13 (1) ◽  
pp. 13-24 ◽  
Author(s):  
Dejan Kihas ◽  
Zeljko Djurovic ◽  
Branko Kovacevic
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Kalman Filter ◽  
Recurrent Neural Network ◽  
Adaptive Filtering ◽  
Adaptive Filter ◽  
Recurrent Neural Networks ◽  
Tracking Model ◽  
Dynamic Identity ◽  
Noise Statistics

Kalman filter is an optimal filtering solution in certain cases, however, it is more often than not, regarded as a non-robust filter. The slight mismatch in noise statistics or process model may lead to large performance deterioration and the loss of optimality. This research paper proposes an alternative method for robust adaptive filtering concerning lack of information of noise statistics. The method is based on the application of recurrent neural networks trained by a dynamic identity observer. The method is explained in details and tested in the case analysis of object tracking model. Performance evaluation is made for cases of the standard Kalman filter, a noise-adaptive Kalman filter, the adaptive filter with a recurrent neural network trained by a static identity observer, and the adaptive filter with recurrent neural network trained by a dynamic identity observer. The results for different noise statistics as well as noise statistics mismatches are compared and presented. It is shown that in cases with a lack of knowledge of the noise statistics it is beneficial to use the filtering method proposed in this research work.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

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